Testing the Mutual Consistency of the Pantheon and SDSS/eBOSS BAO Data Sets with Gaussian Processes

TL;DR

This paper assesses the mutual consistency of BAO and supernova data sets using Gaussian processes, testing their compatibility with the $ m f extit{ extLambda} CDM$ model and reconstructing the expansion history in a model-independent way.

Contribution

It introduces a Gaussian process regression approach to test the consistency of BAO and supernova data sets and explores potential deviations from $ m f extLambda} CDM$.

Findings

No significant preference for models beyond $ m f extLambda} CDM$.

Reconstructed expansion histories can fit data better than $ m f extLambda} CDM$.

Constraints on $ m f extOmega}_k$ and $ m f H_0 r_d$ consistent with $ m f extLambda} CDM$.

Abstract

We test the mutual consistency between the baryon acoustic oscillation measurements from the eBOSS SDSS final release, as well as the Pantheon supernova compilation in a model independent fashion using Gaussian process regression. We also test their joint consistency with the $\Lambda$CDM model, also in a model independent fashion. We also use Gaussian process regression to reconstruct the expansion history that is preferred by these two datasets. While this methodology finds no significant preference for model flexibility beyond $\Lambda$CDM, we are able to generate a number of reconstructed expansion histories that fit the data better than the best-fit $\Lambda$CDM model. These example expansion histories may point the way towards modifications to $\Lambda$CDM. We also constrain the parameters $\Omega_k$ and $H_0r_d$ both with $\Lambda$CDM and with Gaussian process regression. We find that $H_0r_d =10030 \pm 130$ km/s and $\Omega_k = 0.05 \pm 0.10$ for $\Lambda$CDM and that $H_0r_d = 10040 \pm 140$ km/s and $\Omega_k = 0.02 \pm 0.20$ for the Gaussian process case.